Job Type: Contract
Contract Length: 6+ months
Pay Range: $75-80/hour
Start Date: ASAP
Location: Hybrid (Foster City, CA) – Minimum 3 days onsite per week
About the Opportunity:
Our client, a leader in autonomous vehicle technology, is looking for a skilled Software Engineer to join their Perception Attribute Flywheel team. This project involves building and operating an autolabeling pipeline that accelerates human annotation throughput on vehicle attribute classification tasks by leveraging foundation models. This is a high-impact role that requires a self-motivated professional who can hit the ground running, take pride in building reliable, observable, and well-tested data pipelines, and deliver results to accelerate an autonomous vehicle program.
Key Responsibilities & Deliverables:
This role is focused on the successful completion of specific tasks and deliverables. Your responsibilities will include:
- Building the autolabeling pipeline: Ingesting queued tasks from the annotator service, dispatching them to foundation-model APIs (such as Gemini), parsing structured outputs, and writing pre-labels back into the labeling workflow.
- Building the observability layer: Establishing dashboards to monitor per-task latency, per-model cost, per-attribute coverage, and error-modes.
- Executing experiments: Setting up inputs and collecting outputs in formats required by ML engineers for analysis.
- Collaborating with data infrastructure teams to integrate the pipeline cleanly with existing systems.
- Documenting the system: Creating runbooks and ensuring a clean handoff at the end of the engagement.
We are looking for someone with a proven track record of successful contract engagements. The ideal candidate will have:
- 3+ years of backend / data pipeline engineering experience.
- Strong Python programming skills and comfort with C++.
- Large-dataset experience using PySpark or an equivalent framework.
- ML fundamentals, including a strong understanding of model inference, embeddings, structured output, and common evaluation metrics (precision, recall, calibration); you should be able to reason about ML data shapes and integration patterns.
- Proven experience integrating foundation-models (e.g., Gemini, OpenAI, Anthropic) at production scale.
- Excellent written communication skills for design documentation and runbooks.
- Demonstrated ability to work autonomously and manage your own time effectively to meet project goals.
- W2 only (No C2C or 1099 contractors)
